Face Generation¶
In this project, you'll use generative adversarial networks to generate new images of faces.
Get the Data¶
You'll be using two datasets in this project:
- MNIST
- CelebA
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
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"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
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"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
CelebA¶
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.
show_n_images = 25
"""
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"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Preprocess the Data¶
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.
The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).
Build the Neural Network¶
You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputsdiscriminatorgeneratormodel_lossmodel_opttrain
Check the Version of TensorFlow and Access to GPU¶
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Input¶
Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:
- Real input images placeholder with rank 4 using
image_width,image_height, andimage_channels. - Z input placeholder with rank 2 using
z_dim. - Learning rate placeholder with rank 0.
Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
learning_rate = tf.placeholder(tf.float32, None, name='learning_rate')
return inputs_real, inputs_z, learning_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Discriminator¶
Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param images: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
# TODO: Implement Function
alpha = 0.2
dropout = 0.8
with tf.variable_scope('discriminator', reuse=reuse):
# The kernel size is set to 5
x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same',
kernel_initializer=tf.contrib.layers.xavier_initializer(),
activation=tf.nn.relu)
x1 = tf.layers.batch_normalization(x1, training=True)
relu1 = tf.maximum(x1 * alpha, x1) # 14x14x64
x1 = tf.layers.dropout(inputs=relu1, rate=dropout, training=False)
x2 = tf.layers.conv2d(x1, 128, 5, strides=2, padding='same',
kernel_initializer=tf.contrib.layers.xavier_initializer(),
activation=tf.nn.relu)
bn2 = tf.layers.batch_normalization(x2, training=True)
relu2 = tf.maximum(bn2 * alpha, bn2) # 7x7x128
x2 = tf.layers.dropout(inputs=relu2, rate=dropout, training=False)
x3 = tf.layers.conv2d(x2, 256, 5, strides=2, padding='same',
kernel_initializer=tf.contrib.layers.xavier_initializer(),
activation=tf.nn.relu)
bn3 = tf.layers.batch_normalization(x3, training=True)
relu3 = tf.maximum(bn3 * alpha, bn3) # 4x4x256
#Flatten it
# flat = tf.reshape(relu3, (-1, 4*4*256))
flat = tf.contrib.layers.flatten(relu3)
logits = tf.layers.dense(flat, 1)
out = tf.sigmoid(logits)
return out, logits
"""
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"""
tests.test_discriminator(discriminator, tf)
Generator¶
Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
# TODO: Implement Function
alpha = 0.2
dropout = 0.8
#Reuse the parameters when we are generating the samples but that will not be during the training
with tf.variable_scope('generator', reuse = not is_train):
# First fully connected layer
x1 = tf.layers.dense(z, 3*3*512,
kernel_initializer=tf.contrib.layers.xavier_initializer())
# Reshape it to start the convolutional stack
x1 = tf.reshape(x1, (-1, 3, 3, 512))
x1 = tf.layers.batch_normalization(x1, training=is_train)
x1 = tf.maximum(alpha * x1, x1)
# 3x3x512 now
x2 = tf.layers.conv2d_transpose(x1, 512, 4, strides=1, padding='same',
kernel_initializer=tf.contrib.layers.xavier_initializer())
x2 = tf.layers.batch_normalization(x2, training=is_train)
x2 = tf.maximum(alpha * x2, x2)
x2 = tf.layers.dropout(inputs=x2, rate=dropout, training=is_train)
# 3x3x512 now
x3 = tf.layers.conv2d_transpose(x2, 256, 4, strides=2, padding='same',
kernel_initializer=tf.contrib.layers.xavier_initializer())
x3 = tf.layers.batch_normalization(x3, training=is_train)
x3 = tf.maximum(alpha * x3, x3)
x2 = tf.layers.dropout(inputs=x2, rate=dropout, training=is_train)
# 6x6x256 now
x4 = tf.layers.conv2d_transpose(x3, 128, 4, strides=2, padding='valid',
kernel_initializer=tf.contrib.layers.xavier_initializer())
x4 = tf.layers.batch_normalization(x4, training=is_train)
x4 = tf.maximum(alpha * x4, x4)
x4 = tf.layers.dropout(inputs=x4, rate=dropout, training=is_train)
# 14x14x128 now
# Output layer
logits = tf.layers.conv2d_transpose(x4, out_channel_dim, 5, strides=2, padding='same',
kernel_initializer=tf.contrib.layers.xavier_initializer())
# 28x28x3 now
out = tf.tanh(logits)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Loss¶
Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
# TODO: Implement Function
smooth=0.1
g_model = generator(input_z, out_channel_dim, is_train=True)
d_model_real, d_logits_real = discriminator(input_real, reuse=False)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
# Label Smoothing method only on real data labels
# d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=(tf.ones_like(d_model_real)*(1 - smooth))))
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
d_loss = d_loss_real + d_loss_fake
return d_loss, g_loss
"""
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"""
tests.test_model_loss(model_loss)
Optimization¶
Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# TODO: Implement Function
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
g_vars = [var for var in t_vars if var.name.startswith('generator')]
# Optimize
# Without tf.control_dependencies, TensorFlow’s batch normalization layer will not operate correctly during inference
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
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"""
tests.test_model_opt(model_opt, tf)
"""
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"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display. 4, 9, 16...other number will be round to squared numbers, I guess.
:param input_z: Input Z Tensor. It appears here because the inner function "generator" needs it !!!!!
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
def plot_losses(losses):
"""
Plot the discriminator and generator losses on the same graph
:param d_loss: list of loss values for the discriminator
:param g_loss: list of loss values for the generator
"""
d_loss = [item[0] for item in losses]
g_loss = [item[1] for item in losses]
fig, ax = pyplot.subplots()
pyplot.plot(d_loss, label='Discriminator', alpha=0.5)
pyplot.plot(g_loss, label='Generator', alpha=0.5)
pyplot.title("Training Losses")
pyplot.legend()
Train¶
Implement train to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)model_loss(input_real, input_z, out_channel_dim)model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.
- First, load time for recording durations
import time
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
samples, losses = [], [] # samples is temporarily not used
steps = 0 ## =batch_no
print_every=10 ## =stats_every
show_every=100
n_images_to_show = 16
# Build Model, i.e., the tensorflow graph
# A graph defines the computation. It doesn’t compute anything, it doesn’t hold any values, it just defines the operations that you specified in your code.
total_images_count , image_width, image_height, image_channels = data_shape
input_real, input_z, lr = model_inputs(image_width, image_height, image_channels, z_dim)
d_loss, g_loss = model_loss(input_real, input_z, image_channels)
d_train_opt, g_train_opt = model_opt(d_loss, g_loss, lr, beta1)
total_batches = (total_images_count // batch_size) * epoch_count
start_time = time.time()
# Run session
# A session allows to execute graphs or part of graphs. It allocates resources (on one or more machines) for that and holds the actual values of intermediate results and variables
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
# TODO: Train Model
batch_images = batch_images * 2 # Because the original Batch_images scale between -0.5 and 0.5, instead of -1 and 1
steps += 1
# Sample random noise for G
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
# Run optimizers, the generator function will be called twice or thrice since the epoch is fixed to 2 in this project
# Though this strategy seemed not to improve the image quality generated by the generator....
# We need to set the input_real placeholder when we run the generator’s optimizer. The generator doesn’t actually use it, but we’d get an error without it because of the tf.control_dependencies block we created in model_opt function.
_ = sess.run(d_train_opt, feed_dict = {input_real: batch_images, input_z: batch_z, lr: learning_rate})
_ = sess.run(g_train_opt, feed_dict = {input_real: batch_images, input_z: batch_z, lr: learning_rate})
_ = sess.run(g_train_opt, feed_dict = {input_real: batch_images, input_z: batch_z, lr: learning_rate})
_ = sess.run(g_train_opt, feed_dict = {input_real: batch_images, input_z: batch_z, lr: learning_rate})
# An alternative:
# _ = sess.run([d_train_opt, g_train_opt, g_train_opt], feed_dict = {input_real: batch_images, input_z: batch_z, lr: learning_rate})
if steps % print_every == 0:
current_time = time.time()
total_time = current_time - start_time
time_per_batch = total_time / steps
remaining_time = int(time_per_batch * (total_batches - steps))
# At the end of each epoch, get the losses and print them out
train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
train_loss_g = g_loss.eval({input_z: batch_z, input_real: batch_images})
# Save losses to view after training
losses.append((train_loss_d, train_loss_g))
print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
"batch {}/{}...".format(steps, total_batches),
"time/batch...{:.2f}s".format(time_per_batch),
"remaining time...{}s".format(remaining_time),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
if steps % show_every == 0:
show_generator_output(sess, n_images_to_show, input_z, image_channels, data_image_mode)
# show the final output
show_generator_output(sess, n_images_to_show, input_z, image_channels, data_image_mode)
return losses, samples
MNIST¶
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 32
z_dim = 200
learning_rate = 0.0002
beta1 = 0.3
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
losses, _ = train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
plot_losses(losses)
CelebA¶
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 32
z_dim = 200
learning_rate = 0.0002
beta1 = 0.3
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
losses, _ = train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
plot_losses(losses)
Submitting This Project¶
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.